Chen et al. [1]} proposed a two-scan approach, extended from a stripe-based CCL method [2]}, to process stripe extraction and stripe union, respectively.
The first scan can run in parallel by using shared memory, while the second scan is a sequential operation.
ACCL [3]} is another parallelization algorithm that decomposes the image into rows.
By defining a span as a group of pixels that are located contiguously in a row with the same intensity, it spawns two kernels, find spans and merge spans, to label an input image.
The involvement of dynamic parallelism means that good performance can be achieved with this method.
However, it is not suitable to process large images because there is a limitation on the number of threads in one block [4]}.
Yonehara et al. proposed a line-based solution [5]} that improves the local labeling phase of the conventional union-find algorithm [6]} by conducting a row unification using shared memory.
The absence of merge algorithm makes it label an individual section efficiently in the first scan, while the other scans cannot make any further improvements.
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